Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
When Lee Cronin learned about the concept of 3D printers, he had a brilliant idea: why not turn such a device into a universal chemistry set that could make its own drugs?
Professor Lee Cronin is a likably impatient presence, a one-man catalyst. “I just want to get stuff done fast,” he says. And: “I am a control freak in rehab.” Cronin, 39, is the leader of a world-class team of 45 researchers at Glasgow University, primarily making complex molecules. But that is not the extent of his ambition. A couple of years ago, at a TED conference, he described one goal as the creation of “inorganic life”, and went on to detail his efforts to generate “evolutionary algorithms” in inert matter. He still hopes to “create life” in the next year or two.
At the same time, one branch of that thinking has itself evolved into a new project: the notion of creating downloadable chemistry, with the ultimate aim of allowing people to “print” their own pharmaceuticals at home. Cronin’s latest TEDtalk asked the question: “Could we make a really cool universal chemistry set? Can we ‘app’ chemistry?” “Basically,” he tells me, in his office at the university, with half a grin, “what Apple did for music, I’d like to do for the discovery and distribution of prescription drugs.”
The idea is very much at the conception stage, but as he walks me around his labs Cronin begins to outline how that “paradigm-changing” project might progress. He has been in Scotland for 10 years and in that time he has worked hard, as any chemist worth his salt should, to get the right mix of people to produce the results he wants. Cronin’s interest has always been in complex chemicals and the origins of life. “We are pretty good at making molecules. We do a lot of self-assembly at a molecular level,” he says. “We are able to make really large molecules and I was able to get a lot of money in grants and so on for doing that.” But after a while, Cronin suggests, making complex molecules for their own sake can seem a bit limiting. He wanted to find some more life-changing applications for his team’s expertise.
A couple of years ago, Cronin was invited to an architectural seminar to discuss his work on inorganic structures. He had been looking at the way crystals grew “inorganic gardens” of tube-like structures between themselves. Among the other speakers at that conference was a man explaining the possibilities of 3D printing for conventional architectural forms. Cronin wondered if you could apply this 3D principle to structures at a molecular level. “I didn’t want to print an aeroplane, or a jaw bone,” he says. “I wanted to do chemistry.”
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Cronin prides himself on his lateral thinking; his gift for chemistry came fairly late – he stumbled through comprehensive school in Ipswich and initially university – before realising a vocation for molecular chemistry that has seen him make a series of prize-winning, and fund-generating, advances in the field. He often puts his faith in counterintuition. “Confusions of ideas produce discovery,” he says. “People, researchers, always come to me and say they are pretty good at thinking outside the box and I usually think ‘yes, but it is a pretty small box’.” In analyzing how to apply 3D printing to chemistry, Cronin wondered in the first instance if the essentially passive idea of a highly sophisticated form of copying from a software blueprint could be made more dynamic. In his lab, they put together a rudimentary prototype of a chemical 3D printer, which could be programmed to make basic chemical reactions to produce different molecules.
First Complete Structural Study Of A Pegylated Protein
Significant data obtained at NUI Galway reports first crystal structure of a protein modified with a single PEG chain.
Protein PEGylation is a technique routinely used to improve the pharmacological properties of injectable therapeutic proteins. PEG stands for polyethylene glycol, a synthetic polymer that is attached to proteins. The PEG chain artificially increases the size of the protein and improves its retention in the bloodstream. By remaining longer in the blood stream the protein therapeutic is more effective than normal.
Since PEGylation was developed in the 1970s, PEGylated proteins have significantly improved the treatment of several chronic diseases, including hepatitis C, leukemia, arthritis, and Crohn’s disease. PEGylated interferon is one of the most powerful therapeutics used to treat chronic hepatitis. Despite their importance the structure of PEGylated proteins has remained elusive. Now the first crystal structure of a protein modified with a single PEG chain has been determined through research at NUI Galway.
This important research was developed at NUI Galway by Italian PhD student Giada Cattani working with Dr. Peter Crowley, the lead author of the paper. The work also involved collaboration with Dr. Lutz Vogeley from the School of Biochemistry and Immunology at Trinity College Dublin and the crucial X-ray data was collected at the Diamond synchrotron in Oxford, UK.
Commenting on the research findings Dr. Peter Crowley from the School of Chemistry, NUI Galway commented, “The crystal structure reveals an extraordinary double helical arrangement of the protein! It is significant that this data was obtained at NUI Galway, the only Irish University to offer a degree programme in Biopharmaceutical Chemistry. This attractive programme provides training in an area that is essential for the development of new medicines and contributes to the Irish economy.”
A common approach to understand proteins is to crystallize them and determine their structure by using X-ray crystallography. This is necessary to understand what the protein looks like and how it functions. Thousands of research papers have been published about PEGylated proteins. Until the recent findings at NUI Galway there had been no success in crystallizing a PEGylated protein. The knowledge obtained by the Crowley lab has implications for understanding how PEGylated proteins work. The NUI Galway team is also looking at ways to engineer protein assemblies based on this result.
Drugs Go Under Cover as Platelets to Destroy Cancer
Scientists say they have for the first time developed a technique that coats anticancer drugs in membranes made from a patient’s own platelets, allowing the drugs to last longer in the body and attack both primary cancer tumors and the circulating tumor cells that can cause a cancer to metastasize. The work reportedly was tested successfully in an animal model.
“There are two key advantages to using platelet membranes to coat anticancer drugs,” says Zhen Gu, Ph.D., corresponding author of a paper on the work and an assistant professor in the joint biomedical engineering program at North Carolina State University and the University of North Carolina at Chapel Hill. “First, the surface of cancer cells has an affinity for platelets; they stick to each other. Second, because the platelets come from the patient’s own body, the drug carriers aren’t identified as foreign objects, so last longer in the bloodstream.”
“This combination of features means that the drugs can not only attack the main tumor site, but are more likely to find and attach themselves to tumor cells circulating in the bloodstream, essentially attacking new tumors before they start,” adds Quanyin Hu, a Ph.D. student and lead author of the paper (“Anticancer Platelet-Mimicking Nanovehicles”), which appears in Advanced Materials
Here’s how the process works. Blood is taken from a patient (a lab mouse in the case of this research) and the platelets are collected from that blood. The isolated platelets are treated to extract the platelet membranes, which are then placed in a solution with a nanoscale gel containing the anticancer drug doxorubicin (Dox), which attacks the nucleus of a cancer cell.
The solution is compressed, forcing the gel through the membranes and creating nanoscale spheres made up of platelet membranes with Dox-gel cores. These spheres are then treated so that their surfaces are coated with the anticancer drug TRAIL, which is most effective at attacking the cell membranes of cancer cells.
When released into a patient’s bloodstream, these pseudo-platelets can circulate for up to 30 hours as compared to approximately six hours for the nanoscale vehicles without the coating. When one of the pseudo-platelets comes into contact with a tumor, three things happen more or less at the same time.
First, the P-Selectin proteins on the platelet membrane bind to the CD44 proteins on the surface of the cancer cell, locking it into place. Second, the TRAIL on the pseudo-platelet’s surface attacks the cancer cell membrane. Third, the nanoscale pseudo-platelet is effectively swallowed by the larger cancer cell. The acidic environment inside the cancer cell then begins to break apart the pseudo-platelet, thus freeing the Dox to attack the cancer cell’s nucleus.
In a study using mice, the researchers found that using Dox and TRAIL in the pseudo-platelet drug delivery system was significantly more effective against large tumors and circulating tumor cells than using Dox and TRAIL in a nano-gel delivery system without the platelet membrane.
“We’d like to do additional pre-clinical testing on this technique,” notes Dr. Gu. “And we think it could be used to deliver other drugs, such as those targeting cardiovascular disease, in which the platelet membrane could help us target relevant sites in the body.”
CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
Author and Curator: Larry H Bernstein, MD, FCAP
The previous Part II: Cracking the Code of Human Life,
Part II From Molecular Biology to Translational Medicine:How Far Have We Come, and Where Does It Lead Us? Is broken into a three part series.
Part II A. “CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way” reviews the Human Genome Project and the decade beyond.
Part IIB. “CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.
Part IIC. “CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease “ extends the discussion to advances in the management of patients as well as providing a roadmap for pharmaceutical drug targeting.
Part III concludes with Ubiquitin, it’s role in Signaling and Regulatory Control.
This article is a continuation of a previous discussion on the role of genomics in discovery of therapeutic targets titled, Directions for Genomics in Personalized Medicine, which focused on: key drivers of cellular proliferation, stepwise mutational changes coinciding with cancer progression, and potential therapeutic targets for reversal of the process. And it is a direct extension of Cracking the Code of Human Life (Part I): “the initiation phase of molecular biology”.
These articles review a web-like connectivity between inter-connected scientific discoveries, as significant findings have led to novel hypotheses and many expectations over the last 75 years. This largely post WWII revolution has driven our understanding of biological and medical processes at an exponential pace owing to successive discoveries of chemical structure,
the basic building blocks of DNA and proteins,
of nucleotide and protein-protein interactions,
protein folding, allostericity,
genomic structure,
DNA replication,
nuclear polyribosome interaction, and
metabolic control.
In addition, the emergence of methods
for copying,
removal and
insertion, and
improvements in structural analysis as well as
developments in applied mathematics have transformed the research framework.
CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics Computational Genomics I. Three-Dimensional Folding and Functional Organization Principles of The Drosophila Genome Sexton T, Yaffe E, Kenigeberg E, Bantignies F,…Cavalli G. Institute de Genetique Humaine, Montpelliere GenomiX, and Weissman Institute, France and Israel. Cell 2012; 148(3): 458-472. http://dx.doi.org/10.1016/j.cell.2012.01.010/
Chromosomes are the physical realization of genetic information and thus
form the basis for its readout and propagation.
Here we present a high-resolution chromosomal contact map derived from
a modified genome-wide chromosome conformation capture approach
applied to Drosophila embryonic nuclei.
the entire genome is linearly partitioned into
well-demarcated physical domains that
overlap extensively with
active and repressive epigenetic marks.
Chromosomal contacts are hierarchically organized between domains.
Global modeling of contact density and clustering of domains show
that inactive domains are condensed and
confined to their chromosomal territories, whereas
active domains reach out of the territory to form
remote intra- and interchromosomal contacts.
Moreover, we systematically identify specific
long-range intrachromosomal contacts between
Polycomb-repressed domains.
Together, these observations allow for
quantitative prediction of the Drosophila chromosomal contact map,
laying the foundation for detailed studies of
chromosome structure and function in
a genetically tractable system.
Insert pictures
profiles validate the Hi-C Genome wide map
IIC. “Mr. President; The Genome is Fractal !” Eric Lander
(Science Adviser to the President and Director of Broad Institute) et al.
delivered the message on Science Magazine cover (Oct. 9, 2009) and
generated interest in this by the International HoloGenomics Society at
a Sept meeting.
First, it may seem to be trivial to rectify the statement in “About cover”
of Science Magazine by AAAS. The statement “the Hilbert curve is a
one-dimensional fractal trajectory” needs mathematical clarification.
While the paper itself does not make this statement, the new Editorship
of the AAAS Magazine might be even more advanced if the previous
Editorship did not reject (without review) a Manuscript by 20+ Founders
of (formerly) International PostGenetics Society in December, 2006.
Second, it may not be sufficiently clear for the reader that the
reasonable requirement for the DNA polymerase to crawl along
a “knot-free” (or “low knot”) structure does not need fractals. A
“knot-free” structure could be spooled by an ordinary “knitting globule”
(such that the DNA polymerase does not bump into a “knot” when
duplicating the strand; just like someone knitting can go through
the entire thread without encountering an annoying knot): Just to
be “knot-free” you don’t need fractals.
Note, however, that the “strand” can be accessed only at its beginning –
it is impossible to e.g.
to pluck a segment from deep inside the “globulus”.
This is where certain fractals provide a major advantage – that could be
the “Eureka” moment for many readers.
For instance, the mentioned Hilbert-curve is not only “knot free” – but
provides an easy access to “linearly remote” segments of the strand.
If the Hilbert curve starts from the lower right corner and ends at the lower left corner,
for instance the path shows the very easy access of what would be the mid-point
if the Hilbert-curve is measured by
the Euclidean distance along the zig-zagged path.
Likewise, even the path from the beginning of the Hilbert-curve is about equally easy to access –
easier than to reach from the origin a point that is about 2/3 down the path.
The Hilbert-curve provides an easy access between two points
within the “spooled thread”;
from a point that is about 1/5 of the overall length
to about 3/5 is also in a “close neighborhood”.
This may be the “Eureka-moment” for some readers, to realize that
the strand of “the Double Helix” requires quite a finess to fold into
the densest possible globuli (the chromosomes) in a clever way
that various segments can be easily accessed.
Moreover, in a way that distances
between various segments are minimized.
This marvelous fractal structure
is illustrated by the 3D rendering of the Hilbert-curve.
Once you observe such fractal structure, you’ll never again think of
a chromosome as a “brillo mess”, would you?
It will dawn on you that the genome is orders of magnitudes more
finessed than we ever thought so.
Insert picture
profiles validate the Hi-C Genome wide map
Those embarking at a somewhat complex review of some
historical aspects of the power of fractals may wish to consult
the ouvre of Mandelbrot (also, to celebrate his 85th birthday).
For the more sophisticated readers, even the fairly simple
Hilbert-curve (a representative of the Peano-class) becomes
even more stunningly brilliant than just some “see through density”.
Those who are familiar with the classic “Traveling Salesman Problem”
know that “the shortest path along which every given n locations can
be visited once, and only once” requires fairly sophisticated algorithms
(and tremendous amount of computation if n>10 (or much more).
Some readers will be amazed, therefore, that for n=9 the underlying Hilbert-curve
Briefly, the significance of the above realization, that the (recursive)
Fractal Hilbert Curve is intimately connected to the
(recursive) solution of TravelingSalesman Problem,
a core-concept of Artificial Neural Networks summarized below.
Accomplished physicist John Hopfield aroused great excitement in 1982
(already a member of the National Academy of Science)
with his (recursive) design of artificial neural networks and learning algorithms
which were able to find reasonable solutions to combinatorial problems
such as the Traveling SalesmanProblem.
(Book review Clark Jeffries, 1991; 1. J. Anderson, R. Rosenfeld, and
A. Pellionisz (eds.), Neurocomputing 2: Directions for research, MIT
Press, Cambridge, MA, 1990):
“Perceptions were modeled chiefly with neural connections in a
“forward” direction: A -> B -* C — D.
The analysis of networks with strong
backward coupling proved intractable.
All our interesting results arise as consequences of the strong
back-coupling” (Hopfield, 1982).
The Principle of Recursive Genome Function surpassed obsolete
axioms that blocked, for half a Century,
entry of recursive algorithms to interpretation
of the structure-and function of (Holo)Genome.
This breakthrough, by uniting the two largely separate fields of
Neural Networks and Genome Informatics,
is particularly important for those who focused on
Biological (actually occurring) Neural Networks
(rather than abstract algorithms that may not, or
because of their core-axioms, simply could not
represent neural networks under the governance of DNA information).
IIIA. The FractoGene Decade from Inception in 2002 to Proofs of Concept and Impending Clinical Applications by 2012
Junk DNA Revisited (SF Gate, 2002)
The Future of Life, 50th Anniversary of DNA (Monterey, 2003)
Mandelbrot and Pellionisz (Stanford, 2004)
Morphogenesis, Physiology and Biophysics (Simons, Pellionisz 2005)
PostGenetics; Genetics beyond Genes (Budapest, 2006)
ENCODE-conclusion (Collins, 2007)
The Principle of Recursive Genome Function (paper, YouTube, 2008)
You Tube Cold Spring Harbor presentation of FractoGene (Cold Spring Harbor, 2009)
Mr. President, the Genome is Fractal! (2009)
HolGenTech, Inc. Founded (2010)
Pellionisz on the Board of Advisers in the USA and India (2011)
ENCODE – final admission (2012)
Recursive Genome Function is Clogged by Fractal Defects in Hilbert-Curve (2012)
Geometric Unification of Neuroscience and Genomics (2012)
US Patent Office issues FractoGene 8,280,641 to Pellionisz (2012)
When the human genome was first sequenced in June 2000, there were two pretty big surprises.
The first was that humans have only about 30,000-40,000 identifiable genes,
not the 100,000 or more many researchers were expecting.
The lower –and more humbling — number
means humans have just one-third
more genes than a common species of worm.
The second stunner was how much human genetic material — more than 90 percent —
is made up of what scientists were calling “junk DNA.”
The term was coined to describe similar but
not completely identical repetitive sequences of amino acids
(the same substances that make genes),
which appeared to have no function or purpose.
The main theory at the time was that these apparently
non-working sections of DNA were
just evolutionary leftovers, much like our earlobes.
If biophysicist Andras Pellionisz is correct, genetic science
may be on the verge of yielding its third — and
by far biggest — surprise.
With a doctorate in physics, Pellionisz is the holder of Ph.D.’s
in computer sciences and experimental biology from the
prestigious Budapest Technical University and
the Hungarian National Academy of Sciences.
A biophysicist by training, the 59-year-old is a former research
associate professor of physiology and biophysics at New York University,
author of numerous papers in respected scientific journals and textbooks,
a past winner of the prestigious Humboldt Prize for scientific research,
a former consultant to NASA and
holder of a patent on the world’s first artificial cerebellum,
a technology that has already been integrated into research
on advanced avionics systems.
Because of his background, the Hungarian-born brain researcher might
also become one of the first people to successfully launch a new company
by using the Internet to gather momentum for a novel scientific idea.
The genes we know about today, Pellionisz says, can be thought of as something
similar to machines that make bricks (proteins, in the case of genes), with certain
junk-DNA sections providing a blueprint for the
different ways those proteins are assembled.
The notion that at least certain parts of junk DNA might have a purpose for example,
many researchers now refer to
with a far less derogatory term: introns.
Insert picture
3-d-genome-map
In a provisional patent application filed July 31, Pellionisz claims to have
unlocked a key to the hidden role junk DNA plays in growth — and in life itself.
His patent application covers all attempts to
count,
measure and
compare
the fractal properties of introns
for diagnostic and therapeutic purposes.
IIIB. The Hidden Fractal Language of Intron DNA
To fully understand Pellionisz’ idea,
one must first know what a fractal is.
Fractals are a way that nature organizes matter.
Fractal patterns can be found
in anything that has a nonsmooth surface (unlike a billiard ball),
such as coastal seashores,
the branches of a tree or
the contours of a neuron (a nerve cell in the brain).
Some, but not all, fractals are self-similar and
stop repeating their patterns at some stage
the branches of a tree, for example,
can get only so small.
Because they are geometric, meaning they have a shape,
fractals can be described in mathematical terms.
It’s similar to the way a circle can be described
by using a number to represent its radius
(the distance from its center to its outer edge).
When that number is known, it’s possible to draw the circle it represents
without ever having seen it before.
Although the math is much more complicated,
the same is true of fractals.
If one has the formula for a given fractal,
it’s possible to use that formula to construct, or reconstruct,
an image of whatever structure it represents,
no matter how complicated.
The mysteriously repetitive but not identical strands of genetic material
are in reality building instructions organized in
a special type of pattern known as a fractal.
It’s this pattern of fractal instructions, he says, that tells genes what they
must do in order to form living tissue,
everything from the wings of a fly to the entire body of a full-grown human.
In a move sure to alienate some scientists,
Pellionisz has chosen the unorthodox route of
making his initial disclosures online on his own Web site.
He picked that strategy, he says, because
it is the fastest way he can document his claims
and find scientific collaborators and investors.
Most mainstream scientists usually blanch at such approaches,
preferring more traditionally credible methods, such as
publishing articles in peer-reviewed journals.
Basically, Pellionisz’ idea is that
a fractal set of building instructions in the DNA
plays a similar role in organizing life itself.
Decode the way that language works, he says, and
in theory it could be reverse engineered.
Just as knowing the radius of a circle lets one create that circle,
the more complicated fractal-based formula
would allow us to understand how nature creates a heart or
simpler structures, such as disease-fighting antibodies.
At a minimum, we’d get a far better understanding of
how nature gets that job done.
The complicated quality of the idea is helping encourage
new collaborations across the boundaries that sometimes
separate the increasingly intertwined disciplines of
biology, mathematics and computer sciences.
Hal Plotkin, Special to SF Gate. Thursday, November 21, 2002.
An effective strategy to elucidate the signal transduction cascades
activated by a transcription factor is to compare the transcriptional profiles
of wild type and transcription factor knockout models.
Many statistical tests have been proposed for analyzing gene expression data,
but most tests are based on pair-wise comparisons.
Since the analysis of micro-arrays involves the testing of
multiple hypotheses within one study, it is generally accepted that one should
control for false positives by the false discovery rate (FDR).
However, it has been reported that
this may be an inappropriate metric for
comparing data across different experiments.
Here we propose an approach that addresses the above mentioned problem
by the simultaneous testing and integration of the three hypotheses (contrasts)
using the cell means ANOVA model.
These three contrasts test for the effect of a treatment in
wild type,
gene knockout, and
globally over all experimental groups.
We illustrate our approach on microarray experiments that focused
on the identification of candidate target genes and biological processes
governed by the fatty acid sensing transcription factor PPARα in liver.
Compared to the often applied FDR based across experiment comparison,
our approach identified a conservative
but less noisy set of candidate genes
with same sensitivity and specificity.
However, our method had the advantage of properly adjusting for
multiple testing while integrating data from two experiments,
and was driven by biological inference.
We present a simple, yet efficient strategy to compare
differential expression of genes across experiments
while controlling for multiple hypothesis testing.
B. Managing biological complexity across orthologs with a visual knowledge-base
of documented biomolecular interactions Vincent VanBuren & Hailin Chen
Scientific Reports 2, Article number: 1011 http://dx.doi.org:/10.1038/srep01011
Received 02 October 2012 Accepted 04 December 2012
The complexity of biomolecular interactions and influences
is a major obstacle to their comprehension and elucidation.
Visualizing knowledge of biomolecular interactions
increases comprehension and
facilitates the development of new hypotheses.
The rapidly changing landscape of high-content experimental results
also presents a challenge for the maintenance of comprehensive knowledgebases.
Distributing the responsibility for maintenance of a knowledgebase
to a community of subject matter experts is an effective strategy
for large, complex and rapidly changing knowledgebases.
Cognoscente serves these needs by building visualizations for queries
of biomolecular interactions on demand,
by managing the complexity of those visualizations, and by
crowdsourcing to promote the incorporation of current knowledge
from the literature.
Imputing functional associations between
biomolecules and imputing directionality of regulation for those predictions
each require a corpus of existing knowledge as a framework to build upon.
Comprehension of the complexity of this corpus of knowledge
will be facilitated by effective visualizations of
the corresponding biomolecular interaction networks.
was designed and implemented to serve these roles as a knowledgebase
and as an effective visualization tool for systems biology research and education.
Cognoscente currently contains over 413,000 documented interactions,
with coverage across multiple species.
Perl, HTML, GraphViz1, and a MySQL database were used in the development of Cognoscente.
Cognoscente was motivated by the need to update the knowledgebase
of biomolecular interactions at the user level, and
flexibly visualize multi-molecule query results for
heterogeneous interaction types across different orthologs.
Satisfying these needs provides a strong foundation for
developing new hypotheses about regulatory and metabolic pathway topologies.
Several existing tools provide functions that are similar to Cognoscente, so we selected several popular alternatives to assess how their feature sets compare with Cognoscente ( Table 1 ). All databases assessed had easily traceable documentation for each interaction, and included protein-protein interactions in the database.
Most databases, with the exception of BIND, provide an open-access database that can be downloaded as a whole.
Most databases, with the exceptions of EcoCyc and HPRD, provide
support for multiple organisms.
Most databases support web services for
interacting with the database contents programmatically,
whereas this is a planned feature for Cognoscente.
INT, STRING, IntAct, EcoCyc, DIP and Cognoscente provide built-in
visualizations of query results, which we consider
among the most important features for facilitating comprehension of query results.
BIND supports visualizations via Cytoscape.
Cognoscente is among a few other tools that support
multiple organisms in the same query,
protein->DNA interactions, and
multi-molecule queries.
Cognoscente has planned support for
small molecule interactants (i.e. pharmacological agents).
MINT, STRING, and IntAct provide a prediction (i.e. score)
of functional associations, whereas
Cognoscente does not currently support this.
Cognoscente provides support for multiple edge encodings
to visualize different types of interactions in the same display,
a crowdsourcing web portal that allows users to submit
interactions that are then automatically incorporated in the knowledgebase,
and displays orthologs as compound nodes
to provide clues about potential orthologous interactions.
The main strengths of Cognoscente are that it provides a combined feature set that is superior to any existing database, it provides a unique visualization feature for orthologous molecules, and relatively unique support for multiple edge encodings, crowdsourcing, and connectivity parameterization. The current weaknesses of Cognoscente relative to these other tools are that it does not fully support web service interactions with the database, it does not fully support small molecule interactants, and it does not score interactions to predict functional associations. Web services and support for small molecule interactants are currently under development.
Related references from Leaders in Pharmaceutical Intelligence:
Larry, in a series of papers, Fertil, Deschavannes and colleagues have done beautiful analyses of fractal diagrams of Genome sequences in a series of papers.[Deschavanne PJ, Giron A, Vilain J, Fagot G, Fertil B (1999) Mol Biol Evol 16: 1391-1399; Fertil B, Massin M, Lespinats S, Devic C, Dumee P, Giron A (2005) GENSTYLE: exploration and analysis of DNA sequences with genomic signature. Nucleic Acids Res 33(Web Server issue):W512-5]. Clearly this gives an extraordinary insight in the specificity of positional sequence clusters. While fractals work well with octanucleotide clusters, longer the oligonucleotide tracks, higher the resolution. I feel that high resolution fractal maps of fentanucleotide sequences will provide something truely different and may be used as a tool to compare normal cellular DNA sequences to those from cancer cell lines and provide an operational window for manipulations.
Among many important roles of Nitric oxide (NO), one of the key actions is to act as a vasodilator and maintain cardiovascular health. Induction of NO is regulated by signals in tissue as well as endothelium.
The rate of production of NO has been established to be dependent on Wall Shear Stress (WSS) (Mashour and Broock, Brain Res., 1999) . Many mathematical models have been developed as 2D diffusion models to predict distribution of NO transport in single vessels, eg. arterioles (Please see Sources for references ).
Chen et. al. (Med. Biol. Eng. Comp., 2011) developed a 3-D model consisting of two branched arterioles and nine capillaries surrounding the vessels. Their model not only takes into account of the 3-D volume, but also branching effects on blood flow (Please see Fig 1 and Fig 2 from Chen et. al. 2011 ).
Fig. 1 Blood phase separation with vascular branching. RBC fractional flow in daughter branch alpha is not necessarily equal to that in branch beta
The mathematical model considers dynamic characteristics related to blood flow, blood vessel structures and transport mechanism in the wall. The authors have considered effects of branching and ratio of diameters between blood vessels of parent and children to determine the fractional blood flow which gets distributed in the network. These branching effects of the vessels will also affect the blood volume or RBC (Red Blood Cell), hence NO consumption in the blood. Parameters in the model are either obtained or fitted with experimental results from literature. Their model assumes a linear relationship of NO production with wall shear stress which in turn will be regulated by blood flow determined by branching characteristics of blood vessels. Moreover, the mathematical model includes transport of NO through the blood vessels in the tissue (in the defined volume of the model) as diffusion model,. The model was solved using Finite Elements method using the software COMSOL.
Their model results show that wall shear stress changes depending upon the distribution of RBC in the microcirculations of blood vessels, which leads to differential production of NO along the vascular network. Levels of NO at vascular walls can be less in branches which receive more blood flow, due to the balance between higher consumption of NO by RBC and production of NO due to high wall stress. Their 3-D simulations showed the importance of capillaries such that NO can be concentrated in tissues far away in distance from arterioles facilitating much controlled NO regulation.
Though, the 3-D model developed by Chen et. al., (2011) is an idealized mathematical model of blood flow with production and consumption of NO, depending upon WSS, yet it shows importance of structure of blood vessels in distributions of NO in vessels and tissues. Such a model with proper extension to larger network can give more insights into differential distributions of NO as a function of blood flow and wall shear stress. As nano-medicine become sophisticated in years to come, information of distribution of NO in tissues and blood vessels can help the medicine to be more targeted.
Clinical Trials Results for Endothelin System: Pathophysiological role in Chronic Heart Failure, Acute Coronary Syndromes and MI – Marker of Disease Severity or Genetic Determination?
Inhibition of ET-1, ETA and ETA-ETB, Induction of NO production, stimulation of eNOS and Treatment Regime with PPAR-gamma agonists (TZD): cEPCs Endogenous Augmentation for Cardiovascular Risk Reduction – A Bibliography
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